Discussions surrounding AI ethics have been around for more than seven decades. The inception of the famous three laws of robotics by Isaac Asimov dates back to his 1942 short story, Runaround. At that time, AI ethics existed primarily in science fiction. Fast forward to today, and it has evolved into a pressing concern for every AI researcher. The Capgemini Research Institute tells us that ethical dilemmas arising from AI impact at least 9 out of 10 businesses. But what exactly is AI ethics?




As AI technology progresses, the likelihood of encountering ethical challenges increases. Artificial intelligence, designed to emulate human intelligence and decision-making, brings forth numerous risks, particularly concerning human safety. The functionality of AI relies heavily on data. When this data is either inaccurate or biased, it poses the risk of generating subpar or potentially hazardous outcomes. AI ethics encompass a collection of principles and guidelines governing the development and utilisation of AI. Organizations formally integrate these into AI ethics policies to regulate the decisions made by staff and stakeholders. This proactive approach aims to ensure adherence to ethical AI standards, mitigate risks, and ultimately enhance the well-being of all individuals. Successful entrepreneur, investor and philanthropist, Seth Taube, has this to say about AI ethics:


“As investors and consumers, we can harness the power of the market to navigate the uncharted waters of AI with a steady moral compass. Not only will this result in societal well-being being prioritized but also in the creation of a more sustainable technology, one that will not eat its own tail and eventually become the cause for its own obsolescence.”


In essence, there are five key principles of AI ethics – transparency, impartiality, accountability, reliability, and security and privacy.




Starting with recruitment processes all the way to autonomous vehicles, AI plays a crucial role in ensuring human safety and well-being. Consequently, transparency in AI systems becomes paramount. Businesses, customers, and the broader public need to comprehend the inner workings of algorithms and the rationale behind AI-driven decisions. Consider the scenario of a bank rejecting an online loan application. Naturally, the customer seeks an explanation for the algorithmic decision to better understand and potentially enhance their chances of approval in subsequent attempts. The Dutch government is taking a step towards transparency by proposing a register mandating public services throughout The Netherlands to disclose their AI algorithms online. However, some argue that this approach may be ineffective, as most individuals lack the proficiency to interpret the data. Achieving transparency in AI requires developers to articulate clearly how their AI arrives at decisions. Simultaneously, efforts are needed to enhance public understanding of artificial intelligence.




Another fundamental tenet in AI ethics is impartiality, emphasizing the equal treatment of all individuals by AI systems. Achieving this requires the elimination of biases and discrimination from AI systems, a goal that hinges on the use of high-quality data. Many datasets lack a specific design for AI training, potentially carrying over biases and idiosyncrasies from their original collection process. The challenge lies in AI’s inability to discern biases within its data autonomously. Failure to address this issue may result in AI systems perpetuating and automating biases. Numerous instances highlight AI biases contributing to systemic discrimination against marginalized groups. Consequently, researchers must employ unbiased, high-quality data and rigorously test models to identify and rectify any biased behaviour.




Accountability is also pivotal for AI ethics. Given that algorithms are powered by artificial intelligence, it becomes imperative to determine responsibility when things go awry. Accountability should extend beyond the operational phase of AI to encompass every stage of its development, involving individuals and organizations associated with the AI system. In the realm of AI accountability, emphasis is placed on prevention as much as remedy. Teams must comprehensively understand the system’s performance, diligently supervise algorithm development, and carefully curate high-quality data for integration. Seeking insights from diversity experts and end-users of the AI system is paramount for informed decision-making. Additionally, when AI is utilized for sensitive purposes like public services, external reviews should be obligatory to ensure ongoing accountability.




Reliability is a crucial attribute for AI systems, particularly when deployed for critical services like healthcare or credit applications. It ensures that the outcomes generated by the system are not only consistent but also reproducible. The cornerstone of ensuring reliability in AI systems lies in vigilant monitoring. By actively observing the system’s performance, any issues can be promptly identified and reported. This proactive approach allows for the swift implementation of measures to mitigate risks, thereby maintaining the dependability of the AI system.




Establishing security measures is imperative to safeguard sensitive data in AI systems. This involves implementing practices such as data encryption, identifying and fortifying system vulnerabilities, and defending against malicious attacks. Responsible data collection, coupled with robust data governance practices, further reinforces the security framework. Forbes highlights a significant challenge in the AI landscape – namely, the disparate nature of AI systems crafted by a diverse network of creators. This diversity poses obstacles to achieving the requisite levels of accountability, reliability, and security for ethical AI. A unified approach to security throughout the entire lifespan of the AI system is essential to overcome these challenges and attain true security. Lars Reger, Forbes Councils Member, says:


“A big challenge today is that the AI ecosystem is a patchwork of contributions from ranging creators, yet consistency and complete integration are core requirements of ethical AI. Currently, the level of accountability and the amount of trust among contributors are not equal or consistent. If there’s even a tiny vulnerability in the “security and privacy by design” principle, the complete ecosystem could crumble if uncovered by attackers. Therefore, every participant in the development and execution of AI must work toward security that is interoperable and assessable.”




In his latest bestseller, You Don’t Need a £1 Million Cybersecurity Budget, Zhero’s founder and CEO, Izak Oosthuizen, emphasizes the need for ethical practice and process when implementing AI and automation. This includes reliability, transparency, accountability and most important, data privacy and security. Any questions for Izak or Zhero? Please reach out to him. Izak is ready to help and support your AI journey and ensure that it is safe for you, the world and the future.


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